This proposal seeks funding for a newly established Center for Complex Systems at Florida ATlantic University in Boca Raton, Florida. The main scientific goal of the Center is to understand how a complex biological system containing very many degrees of freedom (e.g. neurons, muscles, vascular processes) generates ordered behavior or macroscopic, spatiotemporal patterns. This problem is attacked on several levels of description by virtue of a direct, on-site collaboration between theoretical physics (including computational modeling), psychology and neurobiology. Experimental test fields are pattern generation in biologically significant, multidegree of freedom activities such as speech and limb coordination in humans and the analysis of well-defined rhythmic behaviors (e.g. feeding) in Helisoma and Pleurobranchaea. The theoretical backdrop comes from synergetics, originally a physical theory for the spontaneous formation of pattern in open, nonequilibrium systems, but which now encompasses other fields as well. Synergetics promotes a search for the essential variables of the system under study that are characteristic of its collective state (the so-called order parameters). It is these order parameters and their dynamics (including both deterministic and stochastic aspects) that will be used to explain specific pattern formation phenomena in biology studied here, including: stability and loss of stability leading to behavioral change (e.g. switching among multiple states), synchronization, entrainment and learning. Analytic tools pertaining to the stability of patterns (both kinematic and neuronal) and the time scales on which they persist will be developed, e.g. relaxation time and fluctuational measures. Such observables enter into explicit modeling work that is computationally implemented. A working assumption behind the Center's mission and the present proposal is that the tools and concepts of synergetics (nonlinear dynamics) may be appropriate at both macroscopic behavioral levels (e.g. in patterns among muscles and kinematic events) and at the microscopic scale of neuronal patterns. If so, it may be possible to derive the former from the latter by appropriate coupling among their respective dynamics. Apart from important technological applications, e.g., in prosthetics and robotics, a concerted effort to understand cooperative state and their dynamics in normal behavior seems justified if disordered states are to be better understood.